1. Field of the Invention
The present invention relates generally to registration of images and, more particularly, to a method for blind cross-spectral image registration.
2. Prior Art
Image registration is the process of aligning two images of the same scene so that corresponding points in the scene are placed in identical pixel positions. Standard full-color reproductions use precisely registered images for each of the component colors. Similarly, false color images combine registered image planes from various spectra to reveal important details not readily apparent in the individual images. For remote sensing, registration of infrared to visible spectra is especially important for measuring vegetation, detecting ocean currents, and tracking hot spots in forest fires. Registration of images taken at different times is typically used to identify changes between the images.
The prior art for the problem of image registration generally falls into two different approaches—feature-based and blind. Feature-based registration attempts to identify edges, corner points, contours, or other features that are common to two images, and then uses standard geometric transforms to compute the mapping between the pairs. The problem of identifying those features is complicated by the fact that edge features in infrared images are related to temperature variations, and these edges may not be present in the visible spectrum. Likewise, some features in the visible spectrum may disappear in the infrared spectrum. Consequently, feature-based registration is mainly concerned with locating features common to both images, and rejecting features that are exclusive to one image. The problem becomes difficult when relatively few features are common between the images. For example, a pair of aerial images of an agricultural region may show relatively uniform intensity in the visible spectrum, and highly textured intensity in the infrared spectrum. Each feature evident in the visible spectrum may map to many possible candidates in the infrared image.
The second approach to the problem is to register images blindly by maximizing some criterion that depends on the quality of a candidate registration. The second approach completely avoids the problem of finding a subset of features common to both images, and matching the features to each other. Typical criteria for blind registration are to minimize the sum of squared differences of pixel values or to maximize the normalized correlations of the images. Perhaps the most powerful criterion is the maximization of mutual information which is particularly effective when one image differs from the other in a rather complex way, such as might be observed due to changes in the illumination source position, image modality (X-ray and MRI), or spectral channel (visible and infrared). It has been used effectively in practice to register PET, MR, and CT medical images.
A major potential disadvantage of mutual-information-based methods is the large computational overhead required to compute the joint distributions between two images for many different relative alignments of the images. To overcome this disadvantage, those in the art describe nonlinear iterative methods that reduces substantially the number of different relative alignments that need to be examined. Although the non-linear iterative methods use a sum of square differences of pixel values as the criterion for registration quality, it is known to use mutual-information criterion in its place.
Although the non-linear iterative methods, like all blind-registration algorithms, avoid the cost of identifying corresponding features, the computation is expensive, even in the iterative form of the method. For each relative position of the images considered, a joint distribution of pixel values needs to be computed, which involves a number of operations proportional to the size of the image. Coarse-to-fine techniques known in the art help reduce this cost. Nevertheless, the algorithm must examine several different displacements at maximum detail and many more at lesser detail, and each examination involves access to all of the pixel values at that level of detail.